Naturally though, I wanted to go deeper. After all, the potential data set at my fingertips was incredible – during the course of 2017, I scrobbled (meaning I listened to at least half of a song) nearly 14,000 individual tracks. After searching for a way to access the granular data myself, I noticed that one of the tools that I had previously highlighted called the “Last.FM Mainstream Factor” had a feature that allowed you to save your entire Last.FM listening history as a CSV or XML file. Doing a quick export and then transferring the data into Tableau, I was able to get waist deep into the data and create some more interesting stories and visualizations.

Huge thanks to Last.FM user ghan64 for this tool. You can access it here.

Before we get into the more complicated scenarios, let’s take a look at some of the basics.

Top Albums and Top Artists

This “lollipop” chart shows my most played albums in 2017 based on the # of tracks scrobbled. Two of my favorites from last year, Kesha’s Rainbow and Paramore’s After Laughter both place highly here alongside old favorites (blink-182, Stone Roses, Sgt. Pepper) and newly discovered favorites (Bossanova, Melody’s Echo Chamber, 40 Oz to Freedom).

The chart above shows my top artists of 2017 by number of scrobbles. Each color above each artist’s name refers to a different album of theirs. Thanks to my obsession with the fact that Sgt. Pepper turned fifty last summer, the Beatles were far and away my most played artist, with Green Day, the Rolling Stones, Kesha and the Killers and following behind them. From there, the plays slowly decrease.

Time of Day Listening Trends

When you export your Last.FM data, you’re given a CSV file with UTC-timestamps for every single scrobble you’ve made. This means that if you’re looking to do any time-based analysis, you’ll have to convert the date and time field to your timezone. In Excel, this is actually pretty easy. It also looks like daylight savings time is taken care of for you by Last.FM. Once that’s all set, you can throw all the data into your data tool of choice. Here are some visualizations I created based off of the days/times I scrobbled.

The visualization above represents a 24 hour clock, with each number on the outside of the circle corresponding to an hour. The numbers on the inside correspond with the number of scrobbles during that hour slice over the course of a full year. As you can see, most of my music listening happens during the hours of 8 AM to 5 PM, which roughly corresponds to the time I spend at work with my headphones on. There are some minor fluctuations – note the two hundred scrobble drop that happens at 11 AM and goes through the 12-1 PM period, most likely a result of the fact that I often have meetings where I can’t wear headphones at 11 AM and eat lunch with coworkers at noon.

If we instead color the hour sections by what I’m typically doing at that time of day, the results become even clearer. In the revised visualization above, I’ve filtered out the weekend days to see if my listening patterns became a little cleaner on weekdays where I typically listen to music on my commute to work and at work as well. Here, orange represents time at work, green represents time when I’m getting ready for work or commuting, purple is free time in the evenings, and blue are times where I’m usually asleep. The pattern here is indeed clearer: I’m usually home by 6 PM on weekdays (note the 50%+ drop between the hours of 5-6 PM and 6-7 PM), and I typically wake up at around 7:30 AM each morning and sometimes listen to music while in the shower, which explains the dramatic increase between 6-7 AM and 7-8 AM.

Generally (still not including weekends), the breakdown of my scrobbles is as follows: 68% occur during work hours, 20% during hours where I’m either getting ready for work or commuting, 9% happen in the evening, and 3% happen when I’m out late. Adding the weekends back into the data set only slightly changes these results, with 65% occurring from 9 AM to 5 PM, 19% during normal commuting hours, 12% in the evening and 4% late at night.

After creating these two charts, I was genuinely surprised as to how even my scrobbles were throughout the day. Over the course of a year and nearly 14,000 scrobbles, I suppose it makes sense that things would end up balancing out, at least a little bit.

Day of Week and Other Date Trends

We’ve already figured out that about two-thirds of my scrobbles occur during working hours on the weekdays, but was there any day of the week where I happened to listen to music more?

The short answer? No. In this bubble diagram, the difference between the amount of music that I listen to during the work week versus the weekends is abundantly clear. All said, only 12% of my scrobbles occurred on the weekends. Comparatively, all of the weekdays are nearly even with each other, with Tuesday being the day I listened to the most music (around 20% of scrobbles), Friday, Wednesday and Monday were all extremely similar with about 17-18%.

Expanding to more of a monthly view, it’s clear that the # of scrobbles I had depended on whether or not I was present at work or somewhere else. I was on vacation for two weeks in September, as well as early and late July, and took some time off in both late November and December for the holidays. When I had less time to sit at my desk at work during busier times (especially in November and December), the amount of scrobbles went down dramatically.

Breaking it out by day of the month doesn’t answer any more questions, though the three consecutive high volume days on the 26-28th of the month does garner some attention. My guess would be that those would happen to be days that typically didn’t fall on weekends in 2017 as often as others, but this metric will definitely be more interesting with a larger sample size to look at.

What if we go a step further down the music + data rabbit hole? Next time, I’ll be taking the data from my Last.FM history and combining it with the Echo Nest data from Paul Lamere’s Sort Your Music tool to see if things like weather and sports results have an impact on the kind of music I enjoy listening to. Thanks for reading!

Would be great to release an online tool generating these visualisations when users upload their datasets. One thing I would say, the “clock” disagrams would be much clearer if the radius of the sectors corresponded to the number of scrobbles.